I was bored and wanted to find a way to rank teams statistically. This is obviously limited to the TU/B format. The data I use makes this “useful” for comparison of Ohio teams only. I was inspired by Tyler Benedicts work last year and used some similar data. I ranked teams based a formula using the adjusted PPB (aPPB) and adjusted power percentage (aP%). I adjusted the PPB and P% by using the average of all IS sets.

The formula I used to calculated the ARP (average ranked points ) number I ranked the teams on was:

(aPPB*aP%) + aPPB = ARP

The reason I chose to add aPPB was to prevent teams from having a zero ARP if their power percentage was 0.

Feel feel to critique and add anything you think would make it better! I am open to lots of suggestions.

I ranked middle school teams and high school teams. I did not use the stats for Solon A and Beavercreek A from their hosted tournaments because I know their real A teams did not play. Let me know if I missed stats anywhere!

This looks really cool, Josh (and much more nicely conceived than my brute-force attempts last year)...good to have someone with some quantitative acumen crunching numbers for a change!

In particular, the treatment of power percentage seems better than my ham-fisted way of accounting for it last year, which was to do one set of rankings entirely based on aPPB, and another entirely based on aP%, then add the relative positions of both together (so a team who was 1st on the aP% list and 3rd in aPPB would have a combined ranking of 1+3=4, with 2 then being the best possible number). I think your way here, Josh, is a nice solution to get sound rankings without all that double-listing, position-adding and other nonsense. Kudos to you for calculating all those adjustments!

I made a few changes:1. I pulled data for all of the sets from tournaments outside of Ohio to calculate set adjustments. I was getting some weird adjustments because I did not have enough data points.

2. I decided to calculate APR using the teams BEST adjusted PPB and P% instead of using their CUMULATIVE adjusted PPB and P%. This seems more fair because as teams improve their ranking would be held down by their stats from earlier in the season.

I changed how I calculate adjustments and I am now using the average of a teams top two performances in P% and PPB.

I also labeled each teams region. I used this map

My calculations rely on teams playing IS or MS sets to compare to house writes, I do not have enough data to calculate adjustments so teams that have played CFMS 5/6, CFMS 7/8, SCOP HS, SCOP MS may have a score of 0.

Breakdown By Region (Remember I am ranking every team I have TU/B stats for) Northeast: Number of Teams (76), Avg. ARP (11.61)Northwest: Number of Teams (61), Avg. ARP (9.80)Central: Number of Teams (32), Avg. ARP (15.76)Southeast: Number of Teams (15), Avg. ARP (11.61)Southwest: Number of Teams (43), Avg. ARP (12.75)

Also, if any TD wants the raw data after/before adjustments for bracket placement I would be happy to share.

One thing I just looked at was numbers of the top 16 in each region. The southeast is lacking data right now so I only used the 8 teams that have data, but once your tournament happens I think I will be all set.

I really appreciate all of the work that you have put into the statewide ranking data, it really does do a lot to help promote our sport. It was also interesting to see the state divided up into regions for comparing data. However, why did you organize the state into 5 regions instead of 6 regions (like what we have for OAC). I know that your data is based on NAQT results (and not OAC), however the six region OAC system is the only way that we have organized the state (because as far as I know, we have never had NAQT regional tournaments, just a state tourney). In our case at Little Miami we are in the SW region for OAC, and it would not include teams such as Beavercreek and Northmont because they are in the WC region. Hopefully this doesn't come off as nitpicking, rather just curious about the methodology used. Once again, thanks for all the work you do in putting together state wide data.

On interesting thing I noticed about the middle school stats is that out of 95 NAQT MS set data points there has only been one team to have a PPB over twenty for a tournament all season long. Seem like our MS circuit is a little weak after looking at other MS results.

Overall thoughts I am rethinking power% a little. Some changes I expected did not happen because of the power%. I really expected Boardman to jump to 4th based on their strong PPB this weekend. Joe Esposito and I talked and he suggested using Powers Per 20TUH. I am open to suggestions. I want to incorporate powers somehow. I was also thinking about maybe just adding the adjusted P% to the adjusted PPB. This would cause it to impact the ranking, but not too much.

I didn't think about this as much as William, so this was just a crude way to incorporate P% into the ranking less arbitrarily. Multiplying aP% by two ensures that teams with more powers that tossups benefit. This hurts teams like Miami Valley that generally put up low P%, but I think it does so justifiably. As demonstrated by our performance last year, it is just as (if not more) important to get questions early than to get bonus points. Morlan's system does not address this problem. This is an early version that we have used as the basis for filling out HSQB polls, but this was difficult to do without knowing aP% and not wanting to calculate them.

William's: P/N+(200*aP%+125*aPPB%)/2

This system is based around a 100-point scale. Teams with an aP% above 0.5 will have above 100 in this area. Teams with aPPB% (aPPB/30) above 0.8 (translating to 24 aPPB) will get above 100 in this area. The two are weighted equally for now, although this can be changed to give more emphasis to one or the other, so they are simply averaged. While P/N is generally not a major statistic, adding it to the above value benefits teams that do not neg, but only slightly. The argument is that negs can be important in close games between teams of similar level—adding a number between 0 and 5 or so should not provide a major boost in the rankings, but it can, theoretically, be crucial in determining the outcome of close games. A very high-caliber team (~25 aPPB, ~70 aP%, ~5 P/N) would get around around 127, whereas a good but slightly less competitive (~22.5 aPPB, 40 aP%, 3 P/N) would have around 90. Even if the lower-caliber team had a P/N of 7, their rank would not change significantly.

Having seen William's method, I now prefer it to mine (as it incorporates three variables rather than two), but I can see how others would object to using negs as a statistic. I will be happy to take suggestions or answer questions about both systems!

_________________John John GrogerMiami Valley '20(Usually the one posting)

Rankings "update" after SSNCT.-Nothing has changed since the last update. -Something strange, but promising happened. Teams on average (after outliers were removed) performed 1.2 PPB better at SSNCT than their best IS sets. Since my rankings uses the IS sets as the base (standard difficulty) the adjustment for SSNCT was -1.2 PPB.

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